Non-native English-speaking English language teachers: History and research
Bibliographic record
Abstract
Although the majority of English language teachers worldwide are non-native English speakers, no research was conducted on these teachers until recently. After the pioneering work of Robert Phillipson in 1992 and Peter Medgyes in 1994, nearly a decade had to elapse for more research to emerge on the issues relating to non-native English teachers. The publication in 1999 of George Braine's bookNonnative educators in English language teachingappears to have encouraged a number of graduate students and scholars to research this issue, with topics ranging from teachers' perceptions of their own identity to students' views and aspects of teacher education. This article compiles, classifies, and examines research conducted in the last two decades on this topic, placing a special emphasis on World Englishes concerns, methods of investigation, and areas in need of further attention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".